Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

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2022

Driver Drowsiness Estimation Using EEG Signals with a Dynamical Encoder-Decoder Modeling Framework
Arefnezhad, S., Hamet, J., Eichberger, A., Frühwirth, M., Ischebeck, A., Koglbauer, I. V., Moser, M. & Yousefi, A., Dec 2022, In: Scientific Reports. 12, 1, 1 p., 2650.
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Open Access
Effects of Automation and Fatigue on Drivers from Various Age Groups
Arefnezhad, S., Eichberger, A. & Koglbauer, I. V., Jun 2022, In: Safety. 8, 2, 13 p., 30.
Research output: Contribution to journal › Article › peer-review

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2020
Applying Deep Neural Networks for Multi-level Classification of Driver Drowsiness Using Vehicle-based Measures
Arefnezhad, S., Samiee, S., Eichberger, A., Frühwirth, M., Kaufmann, C. & Klotz, E., 30 Dec 2020, In: Expert Systems with Applications. 162, 12 p., 113778.
Research output: Contribution to journal › Article › peer-review
2019
Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection
Arefnezhad, S., Samiee, S., Eichberger, A. & Nahvi, A., 22 Feb 2019, In: Sensors . 19, 4, 14 p., 943.
Research output: Contribution to journal › Article › peer-review

Open Access

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